Remove generated data add tetrode example

Author: zm711

examples/generated_data.py

@@ -0,0 +1,95 @@
+"""
+Example for usecases.rst
+"""
+
+from itertools import cycle
+import numpy as np
+from quantities import ms, mV, kHz
+import matplotlib.pyplot as plt
+from neo import Block, Segment, ChannelView, Group, SpikeTrain, AnalogSignal
+
+store_signals = False
+
+block = Block(name="probe data", tetrode_ids=["Tetrode #1", "Tetrode #2"])
+block.segments = [
+    Segment(name="trial #1", index=0),
+    Segment(name="trial #2", index=1),
+    Segment(name="trial #3", index=2),
+]
+
+n_units = {"Tetrode #1": 2, "Tetrode #2": 5}
+
+# Create a group for each neuron, annotate each group with the tetrode from which it was recorded
+groups = []
+counter = 0
+for tetrode_id, n in n_units.items():
+    groups.extend([Group(name=f"neuron #{counter + i + 1}", tetrode_id=tetrode_id) for i in range(n)])
+    counter += n
+block.groups.extend(groups)
+
+iter_group = cycle(groups)
+
+# Create dummy data, one segment at a time
+for segment in block.segments:
+
+    # create two 4-channel AnalogSignals with dummy data
+    signals = {
+        "Tetrode #1": AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=10 * kHz, tetrode_id="Tetrode #1"),
+        "Tetrode #2": AnalogSignal(np.random.rand(1000, 4) * mV, sampling_rate=10 * kHz, tetrode_id="Tetrode #2"),
+    }
+    if store_signals:
+        segment.analogsignals.extend(signals.values())
+
+    # create spike trains with dummy data
+    # we will pretend the spikes have been extracted from the dummy signal
+    for tetrode_id in ("Tetrode #1", "Tetrode #2"):
+        for i in range(n_units[tetrode_id]):
+            spiketrain = SpikeTrain(np.random.uniform(0, 100, size=30) * ms, t_stop=100 * ms)
+            # assign each spiketrain to the appropriate segment
+            segment.spiketrains.append(spiketrain)
+            # assign each spiketrain to a given neuron
+            current_group = next(iter_group)
+            current_group.add(spiketrain)
+            if store_signals:
+                # add to the group a reference to the signal from which the spikes were obtained
+                # this does not give a 1:1 correspondance between spike trains and signals,
+                # for that we could use additional groups (and have groups of groups)
+                current_group.add(signals[tetrode_id])
+
+
+# Now plot the data
+
+# .. by trial
+plt.figure()
+for seg in block.segments:
+    print(f"Analyzing segment {seg.index}")
+    stlist = [st - st.t_start for st in seg.spiketrains]
+    plt.subplot(len(block.segments), 1, seg.index + 1)
+    count, bins = np.histogram(stlist)
+    plt.bar(bins[:-1], count, width=bins[1] - bins[0])
+    plt.title(f"PSTH in segment {seg.index}")
+plt.show()
+
+# ..by neuron
+
+plt.figure()
+for i, group in enumerate(block.groups):
+    stlist = [st - st.t_start for st in group.spiketrains]
+    plt.subplot(len(block.groups), 1, i + 1)
+    count, bins = np.histogram(stlist)
+    plt.bar(bins[:-1], count, width=bins[1] - bins[0])
+    plt.title(f"PSTH of unit {group.name}")
+plt.show()
+
+# ..by tetrode
+
+plt.figure()
+for i, tetrode_id in enumerate(block.annotations["tetrode_ids"]):
+    stlist = []
+    for unit in block.filter(objects=Group, tetrode_id=tetrode_id):
+        stlist.extend([st - st.t_start for st in unit.spiketrains])
+    plt.subplot(2, 1, i + 1)
+    count, bins = np.histogram(stlist)
+    plt.bar(bins[:-1], count, width=bins[1] - bins[0])
+    plt.title(f"PSTH blend of tetrode {tetrode_id}")
+plt.show()